Misogyny refers to the deeply ingrained bias against women, characterised by feelings of hatred, aversion, and distrust primarily due to their gender. With a large dataset of Italian-written tweets, our research aims to analyse the individuals who generate and disseminate misogynistic information. To achieve this, we first use a Graph Convolutional Network approach to categorise Twitter accounts into a binary misogyny scheme, leveraging both textual and relational data from friend/follower relationships. Then, we compare the retrieved misogynistic and non-misogynistic communities, considering both network centrality measures and linguistic features.